ECG (Electrocardiogram), which may display the evolution of cardiac electrical activity over time, is one of the important physiological data. Heart rate, rhythm disorders, or morphological changes of electrocardiosignals may be pathological indicators. By analyzing the recorded ECG waveform, myocardial infarction, cardiomyopathy, myocarditis and various other heart diseases may be detected.
In order to monitor long-term ECG signals, a high-performance server is required to provide computing services. When a user submits an enormous amount of electrocardiogram analysis requests simultaneously in an unstable network environment, real-time response is difficult for the traditional cloud platform-based ECG signal analysis. If the analysis task of ECG signals is transferred to the mobile terminal, due to the limited CPU (Central Processing Unit) performance of the mobile terminal, it is still difficult to handle long-term ECG signal processing and make timely feedback. Meanwhile, since the processing needs to consume a large amount of power of the apparatus, for a mobile terminal with limited battery capacity, the battery losses are larger.